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fig6.py
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# -*- coding: utf-8 -*-
# @Author: Theo
# @Date: 2018-06-06 18:38:04
# @Last Modified by: Theo Lemaire
# @Last Modified time: 2019-06-02 11:53:17
''' Sub-panels of the NICE and SONIC computation times comparative figure. '''
import os
import logging
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from argparse import ArgumentParser
from PySONIC.utils import *
from PySONIC.neurons import *
from PySONIC.plt import cm2inch
from utils import *
# Plot parameters
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
matplotlib.rcParams['font.family'] = 'arial'
# Figure basename
figbase = os.path.splitext(__file__)[0]
time_indicators = [1, 60, 60**2, 60**2 * 24, 60**2 * 24 * 7]
time_indicators_labels = ['1 s', '1 min', '1 hour', '1 day', '1 week']
def comptime_vs_amp(neuron, a, Fdrive, amps, tstim, toffset, inputdir, fs=8, lw=2, ps=4):
''' Comparative plot of computation times for different acoustic amplitudes. '''
# Get filepaths
xlabel = 'Amplitude (kPa)'
subdir = os.path.join(inputdir, neuron)
sonic_fpaths = getSims(subdir, neuron, a, nbls.simQueue(
[Fdrive], amps, [tstim], [toffset], [None], [1.], 'sonic'))
full_fpaths = getSims(subdir, neuron, a, nbls.simQueue(
[Fdrive], amps, [tstim], [toffset], [None], [1.], 'full'))
data_fpaths = {'full': full_fpaths, 'sonic': sonic_fpaths}
# Extract computation times (s)
comptimes_fpath = os.path.join(inputdir, '{}_comptimes_vs_amps.csv'.format(neuron))
comptimes = getCompTimesQuant(
inputdir, neuron, amps * 1e-3, xlabel, data_fpaths, comptimes_fpath)
tcomp_lookup = getLookupsCompTime(neuron)
# Extract threshold excitation amplitude
CW_Athr_vs_Fdrive = getCWtitrations_vs_Fdrive(
['RS'], a, [Fdrive], tstim, toffset, os.path.join(inputdir, 'CW_Athrs_vs_freqs.csv'))
Athr = CW_Athr_vs_Fdrive.loc[Fdrive * 1e-3, 'RS']
# Plot comparative profiles of computation times vs. amplitude
fig, ax = plt.subplots(figsize=cm2inch(5.5, 5.8))
plt.subplots_adjust(bottom=0.2, left=0.25, right=0.95, top=0.95)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.set_xlabel(xlabel, fontsize=fs, labelpad=1)
ax.set_ylabel('Computation time (s)', fontsize=fs)
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_ylim((1e-1, 1e6))
for y, lbl in zip(time_indicators, time_indicators_labels):
ax.axhline(y, linewidth=0.5, linestyle='--', c='k')
ax.text(amps.max() * 1e-3, 1.2 * y, lbl, horizontalalignment='right', fontsize=fs)
ax.get_yaxis().set_tick_params(which='minor', size=0)
ax.get_yaxis().set_tick_params(which='minor', width=0)
ax.axhline(tcomp_lookup, color='k', linewidth=lw)
ax.axvline(Athr, linestyle='--', color='k', linewidth=1)
colors = ['silver', 'dimgrey']
for i, key in enumerate(comptimes):
ax.plot(amps * 1e-3, comptimes[key], 'o--', color=colors[i],
linewidth=lw, label=key, markersize=ps)
for item in ax.get_yticklabels():
item.set_fontsize(fs)
for item in ax.get_xticklabels():
item.set_fontsize(fs)
fig.canvas.set_window_title(figbase + 'a')
return fig
def comptime_vs_freq(neuron, a, freqs, CW_Athrs, tstim, toffset, inputdir, fs=8, lw=2, ps=4):
''' Comparative plot of computation times for different US frequencies. '''
# Get filepaths
xlabel = 'Frequency (kHz)'
subdir = os.path.join(inputdir, neuron)
sonic_fpaths, full_fpaths = [], []
for Fdrive in freqs:
Athr = CW_Athrs[neuron].loc[Fdrive * 1e-3] # kPa
Adrive = (Athr + 20.) * 1e3 # Pa
sonic_fpaths += getSims(subdir, neuron, a, nbls.simQueue(
[Fdrive], [Adrive], [tstim], [toffset], [None], [1.], 'sonic'))
full_fpaths += getSims(subdir, neuron, a, nbls.simQueue(
[Fdrive], [Adrive], [tstim], [toffset], [None], [1.], 'full'))
data_fpaths = {'full': full_fpaths, 'sonic': sonic_fpaths}
# Extract computation times (s)
comptimes_fpath = os.path.join(inputdir, '{}_comptimes_vs_freqs.csv'.format(neuron))
comptimes = getCompTimesQuant(
inputdir, neuron, freqs * 1e-3, xlabel, data_fpaths, comptimes_fpath)
tcomp_lookup = getLookupsCompTime(neuron)
# Plot comparative profiles of computation time vs. frequency
fig, ax = plt.subplots(figsize=cm2inch(5.5, 5.8))
plt.subplots_adjust(bottom=0.2, left=0.25, right=0.95, top=0.95)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.set_xlabel(xlabel, fontsize=fs, labelpad=1)
ax.set_ylabel('Computation time (s)', fontsize=fs)
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_ylim((1e-1, 1e6))
for y, lbl in zip(time_indicators, time_indicators_labels):
ax.axhline(y, linewidth=0.5, linestyle='--', c='k')
ax.text(freqs.max() * 1e-3, 1.2 * y, lbl, horizontalalignment='right', fontsize=fs)
ax.get_yaxis().set_tick_params(which='minor', size=0)
ax.get_yaxis().set_tick_params(which='minor', width=0)
ax.axhline(tcomp_lookup, color='k', linewidth=lw)
colors = ['silver', 'dimgrey']
for i, key in enumerate(comptimes):
ax.plot(freqs * 1e-3, comptimes[key], 'o--', color=colors[i],
linewidth=lw, label=key, markersize=ps)
for item in ax.get_yticklabels():
item.set_fontsize(fs)
for item in ax.get_xticklabels():
item.set_fontsize(fs)
fig.canvas.set_window_title(figbase + 'b')
return fig
def comptime_vs_radius(neuron, radii, Fdrive, CW_Athrs, tstim, toffset, inputdir, fs=8, lw=2, ps=4):
''' Comparative plot of computation times for different sonophore radii. '''
# Get filepaths
xlabel = 'Sonophore radius (nm)'
subdir = os.path.join(inputdir, neuron)
sonic_fpaths, full_fpaths = [], []
for a in radii:
Athr = CW_Athrs[neuron].loc[np.round(a * 1e9, 1)] # kPa
Adrive = (Athr + 20.) * 1e3 # Pa
sonic_fpaths += getSims(subdir, neuron, a, nbls.simQueue(
[Fdrive], [Adrive], [tstim], [toffset], [None], [1.], 'sonic'))
full_fpaths += getSims(subdir, neuron, a, nbls.simQueue(
[Fdrive], [Adrive], [tstim], [toffset], [None], [1.], 'full'))
data_fpaths = {'full': full_fpaths, 'sonic': sonic_fpaths}
# Extract computation times (s)
comptimes_fpath = os.path.join(inputdir, '{}_comptimes_vs_radius.csv'.format(neuron))
comptimes = getCompTimesQuant(
inputdir, neuron, radii * 1e9, xlabel, data_fpaths, comptimes_fpath)
tcomp_lookup = getLookupsCompTime(neuron)
# Plot comparative profiles of computation time vs. frequency
fig, ax = plt.subplots(figsize=cm2inch(5.5, 5.8))
plt.subplots_adjust(bottom=0.2, left=0.25, right=0.95, top=0.95)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.set_xlabel(xlabel, fontsize=fs, labelpad=1)
ax.set_ylabel('Computation time (s)', fontsize=fs)
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_ylim((1e-1, 1e6))
for y, lbl in zip(time_indicators, time_indicators_labels):
ax.axhline(y, linewidth=0.5, linestyle='--', c='k')
ax.text(radii.max() * 1e9, 1.2 * y, lbl, horizontalalignment='right', fontsize=fs)
ax.get_yaxis().set_tick_params(which='minor', size=0)
ax.get_yaxis().set_tick_params(which='minor', width=0)
ax.axhline(tcomp_lookup, color='k', linewidth=lw)
colors = ['silver', 'dimgrey']
for i, key in enumerate(comptimes):
ax.plot(radii * 1e9, comptimes[key], 'o--', color=colors[i],
linewidth=lw, label=key, markersize=ps)
for item in ax.get_yticklabels():
item.set_fontsize(fs)
for item in ax.get_xticklabels():
item.set_fontsize(fs)
fig.canvas.set_window_title(figbase + 'c')
return fig
def comptime_vs_DC(neurons, a, Fdrive, Adrive, tstim, toffset, PRF, DCs, inputdir, fs=8, lw=2, ps=4):
''' Comparative plot of computation times for different dity cycles and neuron types. '''
xlabel = 'Duty cycle (%)'
colors = list(plt.get_cmap('Paired').colors[:6])
del colors[2:4]
# Create figure
fig, ax = plt.subplots(figsize=cm2inch(5.5, 5.8))
plt.subplots_adjust(bottom=0.2, left=0.25, right=0.95, top=0.95)
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.set_xlabel(xlabel, fontsize=fs, labelpad=-7)
ax.set_ylabel('Computation time (s)', fontsize=fs)
ax.set_xticks([DCs.min() * 1e2, DCs.max() * 1e2])
ax.set_yscale('log')
ax.set_ylim((1e-1, 1e6))
for y, lbl in zip(time_indicators, time_indicators_labels):
ax.axhline(y, linewidth=0.5, linestyle='--', c='k')
ax.text(DCs.max() * 1e2, 1.2 * y, lbl, horizontalalignment='right', fontsize=fs)
ax.get_yaxis().set_tick_params(which='minor', size=0)
ax.get_yaxis().set_tick_params(which='minor', width=0)
# Loop through neurons
for i, neuron in enumerate(neurons):
# Get filepaths
subdir = os.path.join(inputdir, neuron)
sonic_fpaths = getSims(subdir, neuron, a, nbls.simQueue(
[Fdrive], [Adrive], [tstim], [toffset], [PRF], DCs, 'sonic'))
full_fpaths = getSims(subdir, neuron, a, nbls.simQueue(
[Fdrive], [Adrive], [tstim], [toffset], [PRF], DCs, 'full'))
sonic_fpaths = sonic_fpaths[1:] + [sonic_fpaths[0]]
full_fpaths = full_fpaths[1:] + [full_fpaths[0]]
data_fpaths = {'full': full_fpaths, 'sonic': sonic_fpaths}
# Extract computation times (s)
comptimes_fpath = os.path.join(inputdir, '{}_comptimes_vs_DC.csv'.format(neuron))
comptimes = getCompTimesQuant(
inputdir, neuron, DCs * 1e2, xlabel, data_fpaths, comptimes_fpath)
# Plot
ax.plot(DCs * 1e2, comptimes['full'], 'o--', color=colors[2 * i], linewidth=lw, markersize=ps)
ax.plot(DCs * 1e2, comptimes['sonic'], 'o--', color=colors[2 * i + 1], linewidth=lw,
markersize=ps, label=neuron)
fig.canvas.set_window_title(figbase + 'd')
return fig
def main():
ap = ArgumentParser()
# Runtime options
ap.add_argument('-v', '--verbose', default=False, action='store_true', help='Increase verbosity')
ap.add_argument('-i', '--inputdir', type=str, help='Input directory')
ap.add_argument('-f', '--figset', type=str, nargs='+', help='Figure set', default='all')
ap.add_argument('-s', '--save', default=False, action='store_true',
help='Save output figures as pdf')
args = ap.parse_args()
loglevel = logging.DEBUG if args.verbose is True else logging.INFO
logger.setLevel(loglevel)
inputdir = selectDirDialog() if args.inputdir is None else args.inputdir
if inputdir == '':
logger.error('No input directory chosen')
return
figset = args.figset
if figset == 'all':
figset = ['a', 'b', 'c', 'd']
logger.info('Generating panels {} of {}'.format(figset, figbase))
# Parameters
a = 32e-9 # m
radii = np.array([16, 22.6, 32, 45.3, 64]) * 1e-9 # nm
tstim = 150e-3 # s
toffset = 100e-3 # s
freqs = np.array([20e3, 100e3, 500e3, 1e6, 2e6, 3e6, 4e6]) # Hz
Fdrive = 500e3 # Hz
CW_Athr_vs_Fdrive = getCWtitrations_vs_Fdrive(
['RS'], a, freqs, tstim, toffset, os.path.join(inputdir, 'CW_Athrs_vs_freqs.csv'))
Athr = CW_Athr_vs_Fdrive['RS'].loc[Fdrive * 1e-3]
amps1 = np.array([Athr - 5, Athr, Athr + 20]) * 1e3
amps2 = np.array([50, 100, 300, 600]) * 1e3 # Pa
amps = np.sort(np.hstack([amps1, amps2]))
CW_Athr_vs_radius = getCWtitrations_vs_radius(
['RS'], radii, Fdrive, tstim, toffset, os.path.join(inputdir, 'CW_Athrs_vs_radius.csv'))
Adrive = 100e3 # Pa
PRF = 100 # Hz
DCs = np.array([5, 10, 25, 50, 75, 100]) * 1e-2
# Generate figures
figs = []
if 'a' in figset:
figs.append(comptime_vs_amp('RS', a, Fdrive, amps, tstim, toffset, inputdir))
if 'b' in figset:
figs.append(comptime_vs_freq('RS', a, freqs, CW_Athr_vs_Fdrive, tstim, toffset, inputdir))
if 'c' in figset:
figs.append(comptime_vs_radius(
'RS', radii, Fdrive, CW_Athr_vs_radius, tstim, toffset, inputdir))
if 'd' in figset:
figs.append(comptime_vs_DC(
['RS', 'LTS'], a, Fdrive, Adrive, tstim, toffset, PRF, DCs, inputdir))
if args.save:
for fig in figs:
figname = '{}.pdf'.format(fig.canvas.get_window_title())
fig.savefig(os.path.join(inputdir, figname), transparent=True)
else:
plt.show()
if __name__ == '__main__':
main()

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